Vehicle track detection in synthetic aperture radar coherent change detection (SAR CCD) imagery has application in surveillance and search and rescue. CCD can detect subtle scene changes such as vehicle tracks (e.g., human activity); however, automatic detection of vehicle tracks in SAR CCD is difficult due to areas of low coherence caused by various phenomena other than true change, such as vegetation and the CCD image formation process. Conventional techniques for vehicle track detection in SAR CCD require user cues for identification of roads or other indicators for possible vehicle activity, or explicit modeling of vehicle tracks as parallel low coherence areas. This knowledge can be incorporated into a template that is used to detect candidate vehicle track points, followed by a curve-fitting step to link points. The parallel template causes difficulty in detecting single tracks. These methods may also involve a pre-processing smoothing or thresholding step to mitigate false alarms caused by the non-change phenomena described previously and some can only find one track in an image.